A knowledge-based evolution strategy for the multi-objective minimum spanning tree problem

A fast Knowledge-based Evolution Strategy, KES, for the multi-objective minimum spanning tree, is presented. The proposed algorithm is validated, for the bi-objective case, with an exhaustive search for small problems (4-10 nodes), and compared with a deterministic algorithm, EPDA and NSGA-II for larger problems (up to 100 nodes) using benchmark hard instances. Experimental results show that KES finds the true Pareto fronts for small instances of the problem and calculates good approximation Pareto sets for larger instances tested. It is shown that the fronts calculated by YES are superior to NSGA-II fronts and almost as good as those established by EPDA. KES is designed to be scalable to multi-objective problems and fast due to its small complexity.

FBG optimization using spline encoded evolution strategy

ABSTRACT: This paper presents an encoding scheme adapted for Fiber Bragg Grating (FBG) optimization using metaheuristics. The proposed encoding scheme uses spline approximations in order to build softened refractive index profiles from few encoded parameters. This approach is suitable for Fiber Bragg Grating (FBG) synthesis because it ensures both the reduction of the problem dimensionality and the respect of important restrictions associated to the FBG manufacture. Simulations are shown where an ES using the spline encoding was able to converge faster and produce more interesting filters, when compared with conventional encoding schemes.

Functional promoter analysis using an approach based on an in vitro evolution strategy

In vitro evolution imitates the natural evolution of genes and has been very successfully applied to the modification of coding sequences, but it has not yet been applied to promoter sequences. We propose an alternative method for functional promoter analysis by applying an in vitro evolution scheme consisting of rounds of error-prone PCR, followed by DNA shuffling and selection of mutant promoter activities. We modified the activity in embryogenic sugarcane cells of the promoter region of the Goldfinger isolate of banana streak virus and obtained mutant promoter sequences that showed an average mutation rate of 2.5% after applying one round of error-prone PCR and DNA shuffling. Selection and sequencing of promoter sequences with decreased or unaltered activity allowed us to rapidly map the position of one cis-acting element that influenced promoter activity in embryogenic sugarcane cells and to discover neutral mutations that did not affect promoter Junction. The selective-shotgun approach of this promoter analysis method immediately after the promoter boundaries have been defined by 5' deletion analysis dramatically reduces the labor associated with traditional linker-scanning deletion analysis to reveal the position of functional promoter domains. Furthermore, this method allows the entire promoter to be investigated at once, rather than selected domains or nucleotides, increasing the, prospect of identifying interacting promoter regions.

Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling

Adaptive embedded systems are required in various applications. This work addresses these needs in the area of adaptive image compression in FPGA devices. A simplified version of an evolution strategy is utilized to optimize wavelet filters of a Discrete Wavelet Transform algorithm. We propose an adaptive image compression system in FPGA where optimized memory architecture, parallel processing and optimized task scheduling allow reducing the time of evolution. The proposed solution has been extensively evaluated in terms of the quality of compression as well as the processing time. The proposed architecture reduces the time of evolution by 44% compared to our previous reports while maintaining the quality of compression unchanged with respect to existing implementations. The system is able to find an optimized set of wavelet filters in less than 2 min whenever the input type of data changes.

Enhancing Information Retrieval by Using Evolution Strategies

Similar to Genetic algorithm, Evolution strategy is a process of continuous reproduction, trial and selection. Each new generation is an improvement on the one that went before. This paper presents two different proposals based on the vector space model (VSM) as a traditional model in information Retrieval (TIR). The first uses evolution strategy (ES). The second uses the document centroid (DC) in query expansion technique. Then the results are compared; it was noticed that ES technique is more efficient than the other methods.

Contrasting effects of historical contingency on phenotypic and genomic trajectories during a two-step evolution experiment with bacteria

Background The impact of historical contingency, i.e. the past evolutionary history of a population, on further adaptation is mostly unknown at both the phenotypic and genomic levels. We addressed this question using a two-step evolution experiment. First, replicate populations of Escherichia coli were propagated in four different environmental conditions for 1000 generations. Then, all replicate populations were transferred and propagated for further 1000 generations to a single new environment. Results Using this two-step experimental evolution strategy, we investigated, at both the phenotypic and genomic levels, whether and how adaptation in the initial historical environments impacted evolutionary trajectories in a new environment. We showed that both the growth rate and fitness of the evolved populations obtained after the second step of evolution were contingent upon past evolutionary history. In contrast however, the genes that were modified during the second step of evolution were independent from the previous history of the populations. Conclusions Our work suggests that historical contingency affects phenotypic adaptation to a new environment. This was however not reflected at the genomic level implying complex relationships between environmental factors and the genotype-to-phenotype map.

Shock control bump design optimization on Natural Laminar Aerofoil

The chapter investigates Shock Control Bumps (SCB) on a Natural Laminar Flow (NLF) aerofoil; RAE 5243 for Active Flow Control (AFC). A SCB approach is used to decelerate supersonic flow on the suction/pressure sides of transonic aerofoil that leads delaying shock occurrence or weakening of shock strength. Such an AFC technique reduces significantly the total drag at transonic speeds. This chapter considers the SCB shape design optimisation at two boundary layer transition positions (0 and 45%) using an Euler software coupled with viscous boundary layer effects and robust Evolutionary Algorithms (EAs). The optimisation method is based on a canonical Evolution Strategy (ES) algorithm and incorporates the concepts of hierarchical topology and parallel asynchronous evaluation of candidate solution. Two test cases are considered with numerical experiments; the first test deals with a transition point occurring at the leading edge and the transition point is fixed at 45% of wing chord in the second test. Numerical results are presented and it is demonstrated that an optimal SCB design can be found to significantly reduce transonic wave drag and improves lift on drag (L/D) value when compared to the baseline aerofoil design.

Active transonic aerofoil design optimization using robust multiobjective evolutionary algorithms

The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. The use of adaptive wing/aerofoil designs is being considered, as they are promising techniques in aeronautic/ aerospace since they can reduce aircraft emissions and improve aerodynamic performance of manned or unmanned aircraft. This paper investigates the robust design and optimization for one type of adaptive techniques: active flow control bump at transonic flow conditions on a natural laminar flow aerofoil. The concept of using shock control bump is to control supersonic flow on the suction/pressure side of natural laminar flow aerofoil that leads to delaying shock occurrence (weakening its strength) or boundary-layer separation. Such an active flow control technique reduces total drag at transonic speeds due to reduction of wave drag. The location of boundary-layer transition can influence the position and structure of the supersonic shock on the suction/pressure side of aerofoil. The boundarylayer transition position is considered as an uncertainty design parameter in aerodynamic design due to the many factors, such as surface contamination or surface erosion. This paper studies the shock-control-bump shape design optimization using robust evolutionary algorithms with uncertainty in boundary-layer transition locations. The optimization method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing, and asynchronous evaluation. Two test cases are conducted: the first test assumes the boundary-layer transition position is at 45% of chord from the leading edge, and the second test considers robust design optimization for the shock control bump at the variability of boundary-layer transition positions. The numerical result shows that the optimization method coupled to uncertainty design techniques produces Pareto optimal shock-control-bump shapes, which have low sensitivity and high aerodynamic performance while having significant total drag reduction.

Double-shock control bump design optimization using hybridized evolutionary algorithms

This study investigates the application of two advanced optimization methods for solving active flow control (AFC) device shape design problem and compares their optimization efficiency in terms of computational cost and design quality. The first optimization method uses hierarchical asynchronous parallel multi-objective evolutionary algorithm and the second uses hybridized evolutionary algorithm with Nash-Game strategies (Hybrid-Game). Both optimization methods are based on a canonical evolution strategy and incorporate the concepts of parallel computing and asynchronous evaluation. One type of AFC device named shock control bump (SCB) is considered and applied to a natural laminar flow (NLF) aerofoil. The concept of SCB is used to decelerate supersonic flow on suction/pressure side of transonic aerofoil that leads to a delay of shock occurrence. Such active flow technique reduces total drag at transonic speeds which is of special interest to commercial aircraft. Numerical results show that the Hybrid-Game helps an EA to accelerate optimization process. From the practical point of view, applying a SCB on the suction and pressure sides significantly reduces transonic total drag and improves lift-to-drag (L/D) value when compared to the baseline design.

Robust multidisciplinary UAS design optimisation

There are many applications in aeronautical/aerospace engineering where some values of the design parameters states cannot be provided or determined accurately. These values can be related to the geometry(wingspan, length, angles) and or to operational flight conditions that vary due to the presence of uncertainty parameters (Mach, angle of attack, air density and temperature, etc.). These uncertainty design parameters cannot be ignored in engineering design and must be taken into the optimisation task to produce more realistic and reliable solutions. In this paper, a robust/uncertainty design method with statistical constraints is introduced to produce a set of reliable solutions which have high performance and low sensitivity. Robust design concept coupled with Multi Objective Evolutionary Algorithms (MOEAs) is defined by applying two statistical sampling formulas; mean and variance/standard deviation associated with the optimisation fitness/objective functions. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. It is implemented for two practical Unmanned Aerial System (UAS) design problems; the flrst case considers robust multi-objective (single disciplinary: aerodynamics) design optimisation and the second considers a robust multidisciplinary (aero structures) design optimisation. Numerical results show that the solutions obtained by the robust design method with statistical constraints have a more reliable performance and sensitivity in both aerodynamics and structures when compared to the baseline design.

Adaptive wing/aerofoil design optimisation using MOEA coupled to uncertainty design method

The use of adaptive wing/aerofoil designs is being considered as promising techniques in aeronautic/aerospace since they can reduce aircraft emissions, improve aerodynamic performance of manned or unmanned aircraft. The paper investigates the robust design and optimisation for one type of adaptive techniques; Active Flow Control (AFC) bump at transonic flow conditions on a Natural Laminar Flow (NLF) aerofoil designed to increase aerodynamic efficiency (especially high lift to drag ratio). The concept of using Shock Control Bump (SCB) is to control supersonic flow on the suction/pressure side of NLF aerofoil: RAE 5243 that leads to delaying shock occurrence or weakening its strength. Such AFC technique reduces total drag at transonic speeds due to reduction of wave drag. The location of Boundary Layer Transition (BLT) can influence the position the supersonic shock occurrence. The BLT position is an uncertainty in aerodynamic design due to the many factors, such as surface contamination or surface erosion. The paper studies the SCB shape design optimisation using robust Evolutionary Algorithms (EAs) with uncertainty in BLT positions. The optimisation method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. Two test cases are conducted; the first test assumes the BLT is at 45% of chord from the leading edge and the second test considers robust design optimisation for SCB at the variability of BLT positions and lift coefficient. Numerical result shows that the optimisation method coupled to uncertainty design techniques produces Pareto optimal SCB shapes which have low sensitivity and high aerodynamic performance while having significant total drag reduction.

Molecular genomics of a genetic code alteration

The genetic code is not universal. Alterations to its standard form have been discovered in both prokaryotes and eukaryotes and demolished the dogma of an immutable code. For instance, several Candida species translate the standard leucine CUG codon as serine. In the case of the human pathogen Candida albicans, a serine tRNA (tRNACAGSer) incorporates in vivo 97% of serine and 3% of leucine in proteins at CUG sites. Such ambiguity is flexible and the level of leucine incorporation increases significantly in response to environmental stress. To elucidate the function of such ambiguity and clarify whether the identity of the CUG codon could be reverted from serine back to leucine, we have developed a forced evolution strategy to increase leucine incorporation at CUGs and a fluorescent reporter system to monitor such incorporation in vivo. Leucine misincorporation increased from 3% up to nearly 100%, reverting CUG identity from serine back to leucine. Growth assays showed that increasing leucine incorporation produced impressive arrays of phenotypes of high adaptive potential. In particular, strains with high levels of leucine misincorporation exhibited novel phenotypes and high level of tolerance to antifungals. Whole genome re-sequencing revealed that increasing levels of leucine incorporation were associated with accumulation of single nucleotide polymorphisms (SNPs) and loss of heterozygozity (LOH) in the higher misincorporating strains. SNPs accumulated preferentially in genes involved in cell adhesion, filamentous growth and biofilm formation, indicating that C. albicans uses its natural CUG ambiguity to increase genetic diversity in pathogenesis and drug resistance related processes. The overall data provided evidence for unantecipated flexibility of the C. albicans genetic code and highlighted new roles of codon ambiguity on the evolution of genetic and phenotypic diversity.

The effect of redundancy and neutrality in genetic search

An experimental study aimed at assessing the influence of redundancy and neutrality on the performance of an (1+1)-ES evolution strategy modeled using Markov chains and applied to NK fitness landscapes is presented. For the study, two families of redundant binary representations, one non-neutral family which is based on linear transformations and that allows the phenotypic neighborhoods to be designed in a simple and effective way, and the neutral family based on the mathematical formulation of error control codes are used. The results indicate whether redundancy or neutrality affects more strongly the behavior of the algorithm used.

JML- Based formal development of a Java card application for managing medical appointments

Although formal methods can dramatically increase the quality of software systems, they have not widely been adopted in software industry. Many software companies have the perception that formal methods are not cost-effective cause they are plenty of mathematical symbols that are difficult for non-experts to assimilate. The Java Modelling Language (short for JML) Section 3.3 is an academic initiative towards the development of a common formal specification language for Java programs, and the implementation of tools to check program correctness. This master thesis work shows how JML based formal methods can be used to formally develop a privacy sensitive Java application. This is a smart card application for managing medical appointments. The application is named HealthCard. We follow the software development strategy introduced by João Pestana, presented in Section 3.4. Our work influenced the development of this strategy by providing hands-on insight on challenges related to development of a privacy sensitive application in Java. Pestana’s strategy is based on a three-step evolution strategy of software specifications, from informal ones, through semiformal ones, to JML formal specifications. We further prove that this strategy can be automated by implementing a tool that generates JML formal specifications from a welldefined subset of informal software specifications. Hence, our work proves that JML-based formal methods techniques are cost-effective, and that they can be made popular in software industry. Although formal methods are not popular in many software development companies, we endeavour to integrate formal methods to general software practices. We hope our work can contribute to a better acceptance of mathematical based formalisms and tools used by software engineers. The structure of this document is as follows. In Section 2, we describe the preliminaries of this thesis work. We make an introduction to the application for managing medical applications we have implemented. We also describe the technologies used in the development of the application. This section further illustrates the Java Card Remote Method Invocation communication model used in the medical application for the client and server applications. Section 3 introduces software correctness, including the design by contract and the concept of contract in JML. Section 4 presents the design structure of the application. Section 5 shows the implementation of the HealthCard. Section 6 describes how the HealthCard is verified and validated using JML formal methods tools. Section 7 includes some metrics of the HealthCard implementation and specification. Section 8 presents a short example of how a client-side of a smart card application can be implemented while respecting formal specifications. Section 9 describes a prototype tools to generate JML formal specifications from informal specifications automatically. Section 10 describes some challenges and main ideas came acrorss during the development of the HealthCard. The full formal specification and implementation of the HealthCard smart card application presented in this document can be reached at https://sourceforge.net/projects/healthcard/.

Evolutionary modeling of larval dispersal in blowflies using non-uniform cellular automata

When the food supply flnishes, or when the larvae of blowflies complete their development and migrate prior to the total removal of the larval substrate, they disperse to find adequate places for pupation, a process known as post-feeding larval dispersal. Based on experimental data of the Initial and final configuration of the dispersion, the reproduction of such spatio-temporal behavior is achieved here by means of the evolutionary search for cellular automata with a distinct transition rule associated with each cell, also known as a nonuniform cellular automata, and with two states per cell in the lattice. Two-dimensional regular lattices and multivalued states will be considered and a practical question is the necessity of discovering a proper set of transition rules. Given that the number of rules is related to the number of cells in the lattice, the search space is very large and an evolution strategy is then considered to optimize the parameters of the transition rules, with two transition rules per cell. As the parameters to be optimized admit a physical interpretation, the obtained computational model can be analyzed to raise some hypothetical explanation of the observed spatiotemporal behavior. © 2006 IEEE.